Nowadays image processing techniques have come to be used in a variety of fields.
For example, image processing is performed to address the degradation or modification of images acquired by a video recorder, a digital camera, or the like. In addition, image processing may be performed with a view to clearly understand the pattern of the structure and the structure itself to inspect whether the structure is built as designed.
In medical institutions, a medical image diagnosis apparatus is used to obtain information on tissues in a subject. The medical image diagnosis apparatus creates from the information a medical image such as, for example, a perspective image, a tomographic image, and a blood flow image. The medical image is used for examination and diagnosis. Various types of image processing is performed to modify medical images in a variety of medical image diagnosis apparatuses such as, for example, X-ray computed tomography (CT) systems, magnetic resonance imaging (MRI) equipment, ultrasound diagnosis apparatuses, and X-ray diagnosis apparatuses. The utility of image processing to visualize a blood flow or a flow of a contrast agent, or to extract a lesion, contour of internal organs and the like is widely recognized.
The image processing uses various types of elemental technologies such as noise removal or reduction, feature extraction, and pattern recognition. These technologies are used alone or in combination as appropriate. Among such elemental technologies, a technology to reduce random noise in an image is used to clearly reproduce an object photographed or reconstructed.
However, conventional image processing, in particular noise reduction, requires further improvements. For example, smoothing is widely known as a noise reduction technology. Smoothing refers to processing in which, when an input value f(i, j) is provided for a pixel (i, j), the average density of neighboring pixels around the pixel (i, j) is used as an output value g(i, j) of the pixel (i, j). Specifically, assuming that n×n pixels in the vicinity of the pixel (i, j) are used as the neighboring pixels, the output value g(i, j) is obtained by the following formula (1):
                    [                  Formula          ⁢                                          ⁢          1                ]                                                                      g          ⁡                      (                          i              ,              j                        )                          =                              ∑                          k              =              a                        b                    ⁢                                    ∑                              m                =                c                            d                        ⁢                                          1                                                      (                                          b                      -                      a                      +                      1                                        )                                    ⁢                                      (                                          d                      -                      c                      +                      1                                        )                                                              ·                              f                ⁡                                  (                                                            i                      +                      k                                        ,                                          j                      +                      m                                                        )                                                                                        (        1        )            where a, b, c, and d are integers, and 1/(b−a+1)(d−c+1) is a so-called weight.
However, the simple use of this noise reduction may result in a so-called “edge blur”. The edge blur reduces the spatial resolution of an image, and the entire image is blurred. When the above noise reduction using formula (1) is applied to a medical image described above, for example, even if it is desired that a detailed blood vessel structure be rendered with as little noise as possible, pixels other than those of the blood vessel structure are also averaged (smoothed). That is, while reducing the noise, the smoothing also reduces the contrast that represents the blood vessel structure. As a result, it may be difficult to illustrate the detailed blood vessel structure.
For this reason, there has been proposed an image processing apparatus that calculates the similarity between each pixel and neighboring pixels around the pixel in an image, and obtains a weighted average of them according to the similarity. The image processing apparatus uses the weighted average as a pixel value of the pixel. Thus, the image processing apparatus is capable of both suppressing image blur and reducing noise.
Besides, as with a medical image acquisition device, the image processing apparatus may acquire information on the three-dimensional region of an object. Volume data is generated based on the information on the three-dimensional region thus acquired. The volume data is formed of a three-dimensional array of voxels (pixels). Each voxel is assigned with information (pixel value, etc.) indicating the density or the like of the object in the region.
Further, in the image processing apparatus, noise reduction is performed on volume data as described above. For example, there is an image processing apparatus that performs noise reduction (two-dimensional noise reduction) with respect to any cross section of volume data to thereby achieve noise reduction of the entire volume data by isotropically diffusing the processing contents. At this time, the degree of the diffusion is determined depending on whether the region of interest includes edge information or not. For another example, there is an image processing apparatus that performs noise reduction by averaging or the like (three-dimensional noise reduction) for each voxel in the entire volume data.
Further, in the image processing apparatus, noise reduction is performed between frames acquired in different time phases. For example, the noise reduction is performed by the use of corresponding pixels in the frames acquired in different time phases.
In noise reduction for volume data, in the case of two-dimensional noise reduction, noise may not be sufficiently reduced. Besides, when a different cross section other than the one that is subjected to noise reduction (e.g., a cross section in a different cross-section direction) is observed, there is a case where the noise reduction is not appropriate. In other words, if the object is a human body, information that each voxel has in volume data is unlikely to be uniform. That is, there is likely to be a difference between a cross section subjected to the noise reduction and the different cross section in nature such as the tendency of the distribution of pixel values of pixels. As a result, even if noise reduction is performed on a predetermined cross section in volume data, for example, the noise reduction is not always applicable to a cross section perpendicular to the cross section. Accordingly, signal such as artifacts may appear in a critical area in an image. The same applies to the case where noise reduction is performed between frames acquired in different time phases.
Further, there is a possibility that, when two-dimensional noise reduction is performed a plurality of times on the same cross section to achieve higher noise reduction effect, an edge portion of the image in the cross section is blurred. This is significant in the different cross sections.
On the other hand, in the case of three-dimensional noise reduction, the amount of computation required for the processing is huge. This is, for example, the case that weighted average is performed according to the similarity between each voxel and those around it. As a result, the processing time may be increased by three-dimensional noise reduction.